The role of serotonin 1B in the representation of outcomes
Bibliographic record
Abstract
Abstract Disrupted serotonin neurotransmission has been implicated in the etiology of psychopathic traits. Empirical research has found that people with high levels of psychopathic traits have a deficit in reinforcement learning that is thought to be linked with amygdala dysfunction. Altered serotonin neurotransmission provides a plausible explanation for amygdala dysfunction in psychopathic traits and recent research suggests that this may be associated with serotonin 1B (5-HT1B) receptor function. This research used an animal model to test the hypothesis that 5-HT1B receptors are involved in the encoding of the specific features of reinforcing outcomes. An outcome devaluation task was used to test the effect of the systemic administration of a selective 5-HT1B receptor agonist administered before encoding of “action-outcome” associations. Results showed that while administration of a 5-HT1B receptor agonist allowed rats to acquire instrumental responding for food, when the content of that learning was further probed using an outcome devaluation task, performance differed from controls. 5-HT1B agonism impaired learning about the specific sensory qualities of food rewards associated with distinct instrumental responses, required to direct choice performance when the value of one outcome changed. These findings suggest a role for 5-HT1B receptor function in the encoding of the specific features of reinforcing outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".